On the Testability of Artificial Intelligence and Machine Learning Systems

被引：2
|
作者：
Sangwan, Raghvinder S. [1 ]
Badr, Youakim [2 ]
Srinivasan, Satish [3 ]
Mukherjee, Partha [2 ]
机构：
[1] Penn State Univ, Sch Grad Profess Studies, Software Engn, Malvern, PA 19355 USA
[2] Penn State Univ, Sch Grad Profess Studies, Data Analyt, Malvern, PA 19355 USA
[3] Penn State Univ, Sch Grad Profess Studies, Informat Sci, Malvern, PA 19355 USA
来源：
COMPUTER | 2022年 / 55卷 / 03期
关键词：
Machine learning - Testing;
D O I：
10.1109/MC.2021.3132710
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article examines current testing techniques for the quality assurance of artificial intelligence and machine learning systems. It organizes them based on the granularity of testing level and explores design tactics using these techniques. © 2022 IEEE.
引用
收藏
页码：101 / 105
页数：5
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